Susan E. Walden

Gender Equity in Industrial Engineering: A Pilot Study

We report on findings from a pilot study focused on the Industrial Engineering Department at University of Oklahoma where gender equity has been achieved. The study identifies factors that may contribute to gender parity in engineering and science fields.

“We weren't intentionally excluding them…just old habits”: Women, (lack of) interest and an engineering student competition team

Student, experiential-learning, engineering, competition teams (SELECT) provide an opportunity for engineering students to practice engineering technical and professional skills. The low representation of women in SELECT is often rationalized as a lack of interest by individual women rather than systemic processes that discourage or exclude women. We employ a qualitative-interpretive design and a cultural constructionist lens to bring into focus the interplay of individual interests, understandings of appropriate gender roles, and structural elements that contribute to a culture of inclusion or exclusion. Primary data consist of 90-minute semi-structured interviews of eight team members and one non-member. By interpreting the narrative portraits of two female students, we show the construction of a team culture where in general women are discouraged from participation based on stereotyped gender roles, by night campus attitudes, and by peers who challenge or ignore their skills, contributions, and interests. One woman persevered through the male-dominated culture because she received the encouragement and support of male peers who engaged as comrades and champions. This paper offers recommendations for institutions to demonstrate commitment to equitable access to experiential learning and to nurture student peer cultures that challenge historic gendered ideologies and rhetoric.

Learning the structure of retention data using Bayesian networks

We introduce a novel approach to examining retention data by learning Bayesian Networks automatically from survey data administered to minority students in the College of Engineering at the University of Oklahoma. Bayesian networks provide a human readable model of correlations in large data sets, which enables researchers to improve their understanding of the data without preconceptions. We compare the results of our learned structures with human expectations and interpretation of the data as well as with cross-validation on the data. The average Area Under the Curve of the networks using cross-validation was 0.6. The domain experts believe the methodology of automatically learning such structures is promising and we are continuing to improve the structure learning process.

Engineering Competition Team recruitment and integration strategies impact on team diversity

Engineering Competition Teams (ECT) recruit and integrate new members every year. We interviewed groups of students at two national and international competitions about the methods they use to recruit and retain students, in an effort to understand why ECT have low participation of students from underrepresented groups. None of the teams that were interviewed were especially diverse, in spite of our efforts to interview diverse students. We categorized their recruitment activities into strategies that require direct person interaction with a degree of personal invitation (active) and those that do not (passive) using a theoretical framework provided by Cegler [1]. Not all passive recruiting is done in ways that completely avoid personal interaction. Thus, for ECT, Ceglers passive category was separated into two categories: direct and indirect recruiting. Teams generally had more numerous recruiting strategies than integration strategies. We analyzed integration strategies using the theoretical framework of legitimate peripheral participation [2]. We found that while a handful of teams were using strategies that could fit into this theoretical framework, most were making critical mistakes including trying to integrate too many students and failing to allow new recruits to work at meaningful projects. The paper concludes with recommendations for recruiting and integration practices that may improve team diversity.

Special session — Using qualitative data to bring positive culture into engineering programs

The Research Institute for STEM Education (RISE) examines success in engineering from the perspective of achieving equity of outcome. Equity ensures that opportunities and formal and informal knowledge are available to all students for participation and success in engineering education. Using a qualitative research design that employs semi-structured student interviews as the primary source of data, our findings emphasize the importance of a positive academic culture on the recruitment and retention of engineering students, especially students from historically under-represented groups. The purpose of this session is to engage participants in qualitative data analysis as they review interview narratives and identify evidence in support of RISE research recommendations on positive culture. Participants will use their analysis to develop implementation plans that transfer the recommendations to their institutional contexts.